A LinkedIn For Analytics; Helping Analytic Insights Go Viral In Your Business

It’s been nice to see some recent news coverage of a big data mindset I’m calling “LinkedIn for Analytics.” It’s not related to the LinkedIn company, just inspired by that social media platform. The idea is to bring to the analytics world that same culture of engagement you see on LinkedIn and many other social platforms, or gaming environments.

I wrote in my first Forbes blog post how I will often be referencing the Sentient Enterprise approach to big data analytics. It’s a methodology I developed together with Mohanbir Sawhney, a professor at Northwestern University’s Kellogg School of Management. The Sentient Enterprise is all about helping large organizations retain the agility of a startup, and the LinkedIn for Analytics model is a key element in our methodology.

If you have a fairly small operation, you may be able to survive with a traditional approach like having your centralized team of analysts assign metadata so the rest of the company knows what information is important and where to find it. But in large organizations dealing with big data today, that traditional approach can quickly break down. Humans don’t scale the way data does, and a corps of 100 or even a thousand analysts still won’t be able to keep up with the job of documenting the huge volumes of information and lightning-fast data streams coming at them.

The Era of Crowdsourced Analytics

That’s why we need to turn to the wisdom of the crowd; specifically, the hundreds or thousands of people within your organization who work with data. At its core, LinkedIn for Analytics is essentially analytics on your analytics community, charting their interactions and insights to learn what data is most relevant and useful to the enterprise.

It starts with analytical models to examine what your community of data scientists and other analysts is doing with data, but we’re not simply interested in their specific queries or dashboard activity. We want to provide a forum to capture and analyze commentary and discussion between these folks, complete with social media conventions to let people “like” a certain analytic approach, “follow” a particular analyst or monitor visualizations and data sets that are popular and trending.

Suddenly, these patterns you see in the kinds of ideas, projects and people that get followed, shared and liked help you answer questions such as, “Who are the influencers?” “What projects and ideas are gathering the most energy?” “What does this tell us about the most important projects and their potential success outside the organization?” These insights open up infinite possibilities for innovation within your company!

When we borrow lessons from social media, gaming and other areas where users naturally – even compulsively – want to engage, we can replicate conditions in the IT work environment that encourage that same kind of creativity and dedication around analytics. The end result is a business user community aligned and engaged in the task of tailoring and safely experimenting with data around business problems. Just as with the rest of the social media world, the best solutions go viral.

Many companies have gone in this direction already. When I was at eBay, we created something called The DataHub that supported this kind of collaborative environment. But regardless of your exact approach, the goal is to keep people engaged and invested in data throughout the organization, and to efficiently harvest the insights so that your understanding of data can scale along with your growing data volumes and business operations.